Reconstructing pictures with machine learning [demonstration]

In this post I demonstrate how different techniques of machine learning are working.

The idea used is very simple:

  • each black & white image can be treated as a function of 2 variables - x1 and x2, position of pixel
  • intensity of pixel is output
  • this 2 dimnetional function is very complex
  • we can leave only small fraction of pixels, treating others as 'lost'
  • by looking how different reression algorithms reconstruct picture, we can get some understanding of how those are operating

Don't treat this demonstration as some 'comparison of approaches', because this problem (reconstructing a picture) is very specific and has very few in common with typical ML datasets. And of course, this approach is not to be used in practice to reconstruc pictures :)

I am using scikit-learn and making use of its API, enabling user to construct new models via meta-ensembling and pipelines.

First, we import lots of things

In [1]:
# !pip install image
from PIL import Image
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [2]:
import numpy
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, GradientBoostingRegressor, AdaBoostRegressor

from sklearn.cross_validation import train_test_split
from sklearn.random_projection import GaussianRandomProjection
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.kernel_approximation import RBFSampler
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor

from rep.metaml import FoldingRegressor
from rep.estimators import XGBoostRegressor, TheanetsRegressor

Download the picture

I took quite complex picture with many little details

In [5]:
!wget  -O image.jpg
# !wget -O image.jpg
--2016-02-16 15:40:20--
Connecting to||:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 80728 (79K) [image/jpeg]
Saving to: 'image.jpg'

image.jpg           100%[=====================>]  78.84K  --.-KB/s   in 0.05s  

2016-02-16 15:40:20 (1.41 MB/s) - 'image.jpg' saved [80728/80728]

In [6]:
image = numpy.asarray('./image.jpg')).mean(axis=2)

plt.figure(figsize=[20, 10])
plt.imshow(image, cmap='gray')
<matplotlib.image.AxesImage at 0x1184b6150>

Define a function to train regressor

train_size is how many pixels shall be used in reconstructing the picture. By default, the algorithm will use only 2% of pixels

In [7]:
def train_display(regressor, image, train_size=0.02):
    height, width = image.shape
    flat_image = image.reshape(-1)
    xs = numpy.arange(len(flat_image)) % width
    ys = numpy.arange(len(flat_image)) // width    
    data = numpy.array([xs, ys]).T
    target = flat_image
    trainX, testX, trainY, testY = train_test_split(data, target, train_size=train_size, random_state=42)
    mean = trainY.mean(), trainY - mean)
    new_flat_picture = regressor.predict(data) + mean
    plt.figure(figsize=[20, 10])
    plt.imshow(image, cmap='gray')
    plt.imshow(new_flat_picture.reshape(height, width), cmap='gray')

Linear regression

not very surprising result

In [8]:
train_display(LinearRegression(), image)

Decision tree limited by depth

In [9]:
train_display(DecisionTreeRegressor(max_depth=10), image)